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Found 7 papers, 0 papers with code
How Might We Create Better Benchmarks for Speech Recognition?
The applications of automatic speech recognition (ASR) systems are proliferating, in part due to recent significant quality improvements.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Neural Speech Translation at AppTek
This work describes AppTek’s speech translation pipeline that includes strong state-of-the-art automatic speech recognition (ASR) and neural machine translation (NMT) components.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data
However, there are not enough data sets for accented speech, and for the ones that are already available, more training approaches need to be explored to improve the quality of accented speech recognition.
Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University
AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Neural Simultaneous Speech Translation Using Alignment-Based Chunking
In simultaneous machine translation, the objective is to determine when to produce a partial translation given a continuous stream of source words, with a trade-off between latency and quality.
Cumulative Adaptation for BLSTM Acoustic Models
Further, i-vectors were used as an input to the neural network to perform instantaneous speaker and environment adaptation, providing 8\% relative improvement in word error rate on the NIST Hub5 2000 evaluation test set.
A comprehensive study of batch construction strategies for recurrent neural networks in MXNet
In this work we compare different batch construction methods for mini-batch training of recurrent neural networks.
